Monte Carlo simulation is a versatile method for analyzing the behavior of some activity, plan or process that involves uncertainty. If you face uncertain or variable market demand, fluctuating costs, variation in a manufacturing process, or effects of weather on operations, or if you're investing in stocks, developing a new drug, or drilling an oil well -- you can benefit from using Monte Carlo simulation to understand the impact of uncertainty, and develop plans to mitigate or otherwise cope with risk. This page introduces Monte Carlo simulation and explains why you might need it, and what you need to know (or learn) in order to use it.
What is Monte Carlo Simulation?
The Monte Carlo method was invented by scientists working on the atomic bomb in the 1940s, who named it for the city in Monaco famed for its casinos and games of chance. Its core idea is to use random samples of parameters or inputs to explore the behavior of a complex system or process. Specifically, using a model of the situation, system, or process being looked at, a random sample of each uncertain input (such as sales volume, etc.) is taken. Next the model is recalculated and the key results (such as net profit) saved. this is done repeatedly and the results saved each time. Once complete the range and shape of the results can be examined visually and numerically. Since the 1940's, Monte Carlo methods have been applied to an incredibly diverse range of problems in science, engineering, and finance -- and business applications in virtually every industry.
Why Should I Use Monte Carlo Simulation?
Whenever you need to make an estimate, forecast or decision where there is significant uncertainty, you'd be well advised to consider Monte Carlo simulation -- if you don't, your estimates or forecasts could be way off the mark, with adverse consequences for your decisions! Dr. Sam Savage, a noted authority on simulation and other quantitative methods, says "Many people, when faced with an uncertainty ... succumb to the temptation of replacing the uncertain number in question with a single average value. I call this the flaw of averages, and it is a fallacy as fundamental as the belief that the earth is flat."
Most business activities, plans and processes are too complex for an analytical solution -- just like the physics problems of the 1940s. But you can build a spreadsheet model that lets you evaluate your plan numerically -- you can change numbers, ask 'what if' and see the results. This is straightforward ifyou have just one or two parameters to explore. But many business situations involve uncertainty in many dimensions -- for example, variable market demand, unknown plans of competitors, uncertainty in costs, and many others -- just like the physics problems in the 1940s. If your situation sounds like this, you may find that the Monte Carlo method is surprisingly effective for you as well.
What Knowledge Do I Need to Use It?
To use Monte Carlo simulation, you must be able to build a quantitative model of your business activity, plan or process. One of the easiest and most popular ways to do this is to create aspreadsheet model using Microsoft Excel -- and use Frontline Systems' Risk Solver Platform as a simulation tool. Other ways include writing code in a programming language such as Visual Basic, C++, C# or Java -- with Frontline's Solver SDK Platform -- or using a special-purpose simulation modeling language. You'll also need to learn (or review) the basics of probability and statistics. To deal with uncertainties in your model, you'll replace certain fixed numbers -- for example in spreadsheet cells -- with functions that draw random samples from probability distributions. And to analyze the results of a simulation run, you'll use statistics such as the mean, standard deviation, and percentiles, as well as charts and graphs. Fortunately, there are great software tools (like ours!) to help you do this, backed by technical support and assistance.
How Will This Help Me in My Work or Career?
If your success depends on making good forecasts or managing activities that involve uncertainty, you can benefit in a big way from learning to use Monte Carlo simulation. By doing so, you can avoid the trap of the Flaw of Averages. As Dr. Sam Savage warns, "Plans based on average assumptions will be wrong on average." If you've ever found that projects came in later than you expected, losses were greater than you estimated as "worst case," or forecasts based on averages have gone awry -- you stand to benefit!
Go Beyond the Limits of 'What If' Analysis. A conventional spreadsheet model can take you only so far. If you've created models with best case, worst case and average case scenarios, only to find that the actual outcome was very different, you need Monte Carlo simulation! By exploring thousands of combinations for your 'what-if' factors and you can get deep insight into the range of potential outcomes and how likely each is to occur to allow you to better set expectations and manage risk.
Know What Factors Really Matter. Tools such as Frontline's Risk Solver Platform enable you to quickly identify the high-impact factors in your model, using sensitivity analysis across thousands of Monte Carlo trials. It could take you hours to identify these factors using ordinary 'what if' analysis.
Give Yourself a Competitive Advantage. If you're negotiating a deal, or simply competing in the marketplace, having a realistic idea of the probability of different outcomes -- when your opponent or competitor does not -- can enable you to strike a better bargain, choose the price that yields the most profit, or benefit in other ways.
Be Better Prepared for Executive Decisions. The higher you go in an organization, the more you'll find yourself dealing with uncertainty. Simulation or risk analysis might not be essential for routine day-to-day, low-value decisions -- but you'll find it invaluable as you deal with higher-level, more strategic -- and higher-stakes -- decisions.
Consult our tutorial to learn more. We'll take you step by step through the process of converting a simple spreadsheet model, that uses "flawed average" assumptions, into a risk analysis model that yields surprising insights with the aid of Monte Carlo simulation.
______________________________________________________
0 comments:
Post a Comment